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In order to assess the global evolution of aerosol parameters affecting climate change, a long-term trend analyses of aerosol optical properties were performed on time series from 52 stations situated across five continents. The time series of measured scattering, backscattering and absorption coefficients as well as the derived single scattering albedo, backscattering fraction, scattering and absorption Ångström exponents covered at least 10 years and up to 40 years for some stations. The non-parametric seasonal Mann-Kendall (MK) statistical test associated with several prewhitening methods and with the Sen's slope were used as main trend analysis methods. Comparisons with General Least Mean Square associated with Autoregressive Bootstrap (GLS/ARB) and with standard Least Mean Square analysis (LMS) enabled confirmation of the detected MK statistically significant trends and the assessment of advantages and limitations of each method. Currently, scattering and backscattering coefficients trends are mostly decreasing in Europe and North America and are not statistically significant in Asia, while polar stations exhibit a mix of increasing and decreasing trends. A few increasing trends are also found at some stations in North America and Australia. Absorption coefficients time series also exhibit primarily decreasing trends. For single scattering albedo, 52 % of the sites exhibit statistically significant positive trends, mostly in Asia, Eastern/Northern Europe and Arctic, 18 % of sites exhibit statistically significant negative trends, mostly in central Europe and central North America, while the remaining 30 % of sites have trends, which are not statistically significant. In addition to evaluating trends for the overall time series, the evolution of the trends in sequential 10 year segments was also analyzed. For scattering and backscattering, statistically significant increasing 10 year trends are primarily found for earlier periods (10 year trends ending in 2010-2015) for polar stations and Mauna Loa. For most of the stations, the present-day statistically significant decreasing 10 year trends of the single scattering albedo were preceded by not statistically significant and statistically significant increasing 10 year trends. The effect of air pollution abatement policies in continental North America is very obvious in the 10 year trends of the scattering coefficient - there is a shift to statistically significant negative trends in 2010-2011 for all stations in the eastern and central US. This long-term trend analysis of aerosol radiative properties with a broad spatial coverage enables a better global view of potential aerosol effects on climate changes. [ABSTRACT FROM AUTHOR]

The Fourier transform infrared (FTIR) spectra of fine particulate matter (PM2.5) contain many important absorption bands relevant for characterizing organic matter (OM) and obtaining organic matter to organic carbon (OM/OC) ratios. However, extracting this information quantitatively - accounting for overlapping absorption bands and relating absorption to molar abundance - poses several challenges. For instance, a subset of model parameters lead to calibrations that test almost indistinguishably well against laboratory standards generate substantially different predictions in ambient samples. Furthermore, additional parameters related to molecular structure are required to estimate carbon content from functional group (FG) abundance. However, since many carbon atoms can be branched (not fully functionalized) or polyfunctional, these parameters are not well constrained for ambient sample mixtures. In this work, we present a probabilistic framework to characterize combinations of these parameters that are consistent with field measurements of organic carbon (OC), for which estimates from thermal optical reflectance (TOR) measurements are used. Uncertainties in this probabilistic framework characterize the plausibility of many different parameter values that yield acceptable predictions (to the extent that they can be evaluated) neglected in conventional estimates of statistical uncertainties. Based on calibrations of aliphatic CH, alcohol COH, carboxylic acid COO, carboxylate COO, and amine NH2, we find model parameters for approximately homogeneous groups of samples determined from cluster analysis of FTIR spectra available for 17 sites in the Interagency Monitoring of Protected Visual Environments (IMPROVE) monitoring network (7 sites in 2011 and 10 additional sites in 2013). These groups are interpreted as being predominantly influenced by dust, residential wood burning, wildfire, urban sources, and biogenic aerosols. The resulting calibrations reproduce TOR OC concentrations (R² = 0.96) and provide OM/OC values consistent with our current best estimate of ambient OC. The mean OM/OC ratios corresponding to sample types determined from cluster analysis range between 1.4 and 2.0, though ratios for individual samples exhibit a larger range. Trends in OM/OC for sites aggregated by region or year are compared with another regression approach for estimating OM/OC ratios from a mass balance of the major chemical species contributing to PM fine mass. Differences in OM/OC estimates are observed according to estimation method and are explained through the sample types determined from spectral profiles of the PM. [ABSTRACT FROM AUTHOR]

Particulate matter with aerodynamic diameter less than 2.5 μm (PM 2.5 ) is a complex mixture of chemical constituents emitted from various emission sources or through secondary reactions/processes; however, PM 2.5 is regulated mostly based on its total mass concentration. Studies to identify the impacts on climate change, visibility degradation and public health of different PM 2.5 constituents are hindered by limited ground measurements of PM 2.5 constituents. In this study, national models were developed based on random forest algorithm, one of machine learning methods that is of high predictive capacity and able to provide interpretable results, to predict concentrations of PM 2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) across the conterminous United States from 2005 to 2015 at the daily level. The random forest models achieved high out-of-bag (OOB) R 2 values at the daily level, and the mean OOB R 2 values were 0.86, 0.82, 0.71 and 0.75 for sulfate, nitrate, OC and EC, respectively, over 2005-2015. The long-term temporal trends of PM 2.5 sulfate, nitrate, OC and EC predictions agreed well with their corresponding ground measurements. The annual mean of predicted PM 2.5 sulfate and EC levels across the conterminous United States decreased substantially from 2005 to 2015; while concentrations of predicted PM 2.5 nitrate and OC decreased and fluctuated during the study period. The annual prediction maps captured the characterized spatial patterns of the PM 2.5 constituents. The distributions of annual mean concentrations of sulfate and nitrate were generally regional in the extent that sulfate decreased from east to west smoothly with enhancement in California and nitrate had higher concentration in Midwest, Metro New York area, and California. OC and EC had regional high concentrations in the Southeast and Northwest as well as localized high levels around urban centers. The spatial patterns of PM 2.5 constituents were consistent with the distributions of their emission sources and secondary processes and transportation. Hence, the national models developed in this study could provide supplementary evaluations of spatio-temporal distributions of PM 2.5 constituents with full time-space coverages in the conterminous United States, which could be beneficial to assess the impacts of PM 2.5 constituents on radiation budgets and visibility degradation, and support exposure assessment for regional to national health studies at county or city levels to understand the acute and chronic toxicity and health impacts of PM 2.5 constituents, and consequently provide scientific evidence for making targeted and effective regulations of PM 2.5 pollution., (Copyright © 2018 Elsevier Ltd. All rights reserved.)